class_docstr: A class for representing and solving predictive models based on
  tractography solutions.
  
  Notes
  -----
  This is an implementation of the LiFE model described in [1]_
  
  [1] Pestilli, F., Yeatman, J, Rokem, A. Kay, K. and Wandell
      B.A. (2014). Validation and statistical inference in living
      connectomes. Nature Methods.
docstr: Parameters
  ----------
  gtab : a GradientTable class instance
docstr_tok: ["Parameters","--","--","--","--","--","gtab",":","a","GradientTable","class","instance"]
code: def __init__(self, gtab):
      """
      Parameters
      ----------
      gtab : a GradientTable class instance
  
      """
      # Initialize the super-class:
      ReconstModel.__init__(self, gtab)
  
imports: import numpy
  import scipy.sparse
  import scipy.linalg
  from dipy.reconst.base import ReconstModel, ReconstFit
  from dipy.tracking.utils import unique_rows
  from dipy.tracking.streamline import transform_streamlines
  from dipy.tracking.vox2track import _voxel2streamline
  import dipy.data
  import dipy.core.optimize
label_desc: Use image node_id 1 to create a new FiberModel object from inherited base classes: ReconstModel with example: obj = FiberModel(gtab)